Anterior|Siguiente

Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images

  • role： First author第一作者
  • Email：zhangleigis@outlook.com
  • Introduction：张磊，1993年生，男，硕士研究生，研究方向为遥感技术及地学分析应用。E-mail：zhangleigis@outlook.com
Lei ZHANG,  
  • role： Corresponding author通信作者
  • Email：gongzhn@163.com
  • Introduction：宫兆宁，1976年生，女，副教授，研究方向为遥感技术及地学分析应用。E-mail：gongzhn@163.com
Zhaoning GONG*,  
  • No information about the author is available
Qiwei WANG,  
  • No information about the author is available
Diandian JIN,  
  • No information about the author is available
Xing WANG

resumen

Estuary wetland is a special wetland type, and the extraction of estuary wetland information plays an important role in wetland conservation and scientific research. In this study, Yellow River Delta wetlands, as a typical estuary wetland in the north part of China, are considered the study area. The random forest method, which has evident advantages in feature selection and classification, was chosen to extract wetland information from the study area. First, five different characteristic variables, namely, spectral features, vegetation index, water index, red edge index, and texture features, were generated based on Sentinel-2 data with rich multi-temporal and spectral information. Then, six different classification schemes were constructed based on the preceding characteristic information. Finally, random forest classifier was used to extract the wetland information of the Yellow River Delta and verify the extraction accuracy of different results. The purpose is to select the best plan to improve the effect of wetland information extraction. Results are as follows: (1) The effective use of multiple feature variables is the key to improving the extraction of wetland information. The contribution of different characteristics to the wetland information extraction is described as follows: the red edge index > vegetation index and water index > spectral feature > texture feature. (2) The preferred features based on the random forest algorithm are crucial to extraction accuracy, with an overall accuracy of up to 90.93%, and Kappa coefficient of 0.90. This result shows that the random forest algorithm can effectively process feature selection. In feature variable data mining, the accuracy of the wetland information extraction can be guaranteed, and the operation efficiency can be improved. This study also provides a new idea, method, and technology for the selection of data sources and feature and method selections for wetland information extraction.

palabra clave

estuarine wetland;information extraction;Sentinel-2;Random Forest;feature selection;red edge index;multi-temporal data

References

  1. 1.
    Abuelgasim A A, Gopal S, Irons J R and Strahler A H. 1996. Classification of ASAS multiangle and multispectral measurements using artificial neural networks. Remote Sensing of Environment, 57(2): 79–87
  2. 2.
    Baker C, Lawrence R, Montagne C and Patten D. 2006. Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree-based models. Wetlands, 26(2): 465–474
  3. 3.
    Bao Y H and Ren J B. 2011. Wetland landscape classification based on the BP neural network in DaLinor Lake area. Procedia Environmental Sciences, 10: 2360–2366
  4. 4.
    Barnes E M, Clarke T R, Richards S E, Colaizzi P D, Haberland J, Kostrzewski M, Waller P, Choi C, Riley E, Thompson T, Lascano R J, Li H and Moran M S. 2000. Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data//Proceedings of the 5th International Conference on Precision Agriculture. South Segoe Road, Madison: American Society of Agronomy.
  5. 5.
    Breiman L. 2001. Random forest. Machine Learning, 45(1): 5–32
  6. 6.
    Davranche A, Lefebvre G and Poulin B. 2010. Wetland monitoring using classification trees and SPOT-5 seasonal time series. Remote Sensing of Environment, 114(3): 552–562
  7. 7.
    Delegido J, Verrelst J, Alonso L and Moreno J. 2011. Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11(7): 7063–7081
  8. 8.
    Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P, Meygret A, Spoto F, Sy O, Marchese F and Bargellini P. 2012. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120: 25–36
  9. 9.
    Fernández-Manso A, Fernández-Manso O and Quintano C. 2016. SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity. International Journal of Applied Earth Observation and Geoinformation, 50: 170–175
  10. 10.
    Genuer R, Poggi J M and Tuleau-Malot C. 2010. Variable selection using random forests. Pattern Recognition Letters, 31(14): 2225–2236
  11. 11.
    Gitelson A A, Gritz Y and Merzlyak M N. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3): 271–282
  12. 12.
    Gitelson A and Merzlyak M N. 1994. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, 143(3): 286–292
  13. 13.
    Gong N, Niu Z G, Qi W and Zhaug H Y. 2016. Driving forces of wetland change in China. Journal of Remote Sensing, 20(2): 172–183 (
  14. 14.
    Hill M J. 2013. Vegetation index suites as indicators of vegetation state in grassland and savanna: an analysis with simulated SENTINEL 2 data for a North American transect. Remote Sensing of Environment, 137: 94–111
  15. 15.
    Hou Q Q, Wang F and Yan L. 2013. Extraction of color image texture feature based on gray-level co-occurrence matrix. Remote Sensing for Land and Resources, 25(4): 26–32 (
  16. 16.
    Hu Y F, Deng J J, Kuang X H, Wang P, He S and Xiong L. 2011. Study on land use classification of high resolution remote sensing image based on texture feature. Geography and Geo-Information Science, 27(5): 42–45, 68 (
  17. 17.
    Huang J L. 1999. The area change and succession of Dongtinghu wetland. Geographical Research, 18(3): 297–304 (
  18. 18.
    Huang K, Shida Z S, Deng C L and Du K. 2012. Response Relationship Between Ecosystem and Runoff into the Sea in the Yellow River Estuary. Yellow River, 34(11): 66–69 (
  19. 19.
    Iverson L R, Prasad A M, Matthews S N and Peters M. 2008. Estimating potential habitat for 134 eastern US tree species under six climate scenarios. Forest Ecology and Management, 254(3): 390–406
  20. 20.
    Khatami R, Mountrakis G and Stehman S V. 2016. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: general guidelines for practitioners and future research. Remote Sensing of Environment, 177: 89–100
  21. 21.
    Korhonen L, Hadi, Packalen P and Rautiainen M. 2017. Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index. Remote Sensing of Environment, 195: 259–274
  22. 22.
    Liu H Y, Lv X G and Zhang S K. 2004. Landscape biodiversity of wetlands and their changes in 50 years in watersheds of the Sanjiang Plain. Acta Ecologica Sinica, 24(7): 1472–1479 (
  23. 23.
    Liu L, Han M, Liu Y B and Pan B. 2017. Spatial distribution of wetland vegetation biomass and its influencing factors in the Yellow River Delta Nature Reserve. Acta Ecologica Sinica, 37(13): 4346–4355 (
  24. 24.
    Liu M M, Liu Y L, Sun G Q and Peng L. 2014. SVM land cover classification based on spectral and textural features using stratified samples. Remote Sensing Technology and Application, 29(2): 315–323 (
  25. 25.
    Liu R H, Liang S C, Zhao H Y, Qi G C, Li L X, Jiang Y and Niu Z G. 2017. Progress of Chinese coastal wetland based on remote sensing. Remote Sensing Technology and Application, 32(6): 998–1011 (
  26. 26.
    Liu S, Jiang Q G, Ma Y, Xiao Y, Li Y H and Cui C. 2017. Object-oriented wetland classification based on hybrid feature selection method combining with relief F, multi-objective genetic algorithm and random forest. Transactions of the Chinese Society for Agricultural Machinery, 48(1): 119–127 (
  27. 27.
    Mou X J, Liu X T, Yan B X and Cui B S. 2015. Classification system of coastal wetlands in China. Wetland Science, 13(1): 19–26 (
  28. 28.
    Na X D, Zhang S Q, Kong B, Yu H, Li X F and Liu C Y. 2008. The extraction of freshwater marsh wetland information based on decision tree algorithm——A case study in the Northeast of the Sanjiang Plain. Remote Sensing Technology and Application, 23(4): 365–372 (
  29. 29.
    Na X D, Zhang S Q, Zhang H Q, Li X F, Yu H and Liu C Y. 2009. Integrating TM and ancillary geographical data with classification trees for land cover classification of marsh area. Chinese Geographical Science, 19(2): 177–185
  30. 30.
    Özesmi S L. 2000. Satellite Remote Sensing of Wetlands and a Comparison of Classification Techniques. Twin Cities, MN: University of Minnesota
  31. 31.
    Pu R L and Landry S. 2012. A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species. Remote Sensing of Environment, 124: 516–533
  32. 32.
    Shang J L, Liu J G, Ma B L, Zhao T, Jiao X F, Geng X Y, Huffman T, Kovacs J M and Walters D. 2015. Mapping spatial variability of crop growth conditions using RapidEye data in Northern Ontario, Canada. Remote Sensing of Environment, 168: 113–125
  33. 33.
    Shoko C and Mutanga O. 2017. Examining the strength of the newly-launched Sentinel 2 MSI sensor in detecting and discriminating subtle differences between C3 and C4 grass species. ISPRS Journal of Photogrammetry and Remote Sensing, 129: 32–40
  34. 34.
    Song H L. 2015. Influence of reclamation activities on ecosystem type and carbon sink function of the coastal wetland in the Yellow River estuary. Changchun: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences.
  35. 35.
    Su W, Zhang M Z, Jiang K P, Zhu D H, Huang J X and Wang P X. 2018. Atmospheric correction method for sentinel-2 satellite imagery. Acta Optica Sinica, 38(1): 128001 (
  36. 36.
    Wang L, Dronova I, Gong P, Yang W B, Li Y R and Liu Q. 2012. A new time series vegetation–water index of phenological–hydrological trait across species and functional types for Poyang Lake wetland ecosystem. Remote Sensing of Environment, 125: 49–63
  37. 37.
    Wang X H, Li Y Z, Meng H, Dong H F, Guo Y and Tong S Z. 2015. Distribution pattern of plant community in new-born coastal wetland in the Yellow River Delta. Scientia Geographica Sinica, 35(8): 1021–1026 (
  38. 38.
    Wen Q K, Zhang Z X, Xu J Y, Zuo L J, Wang X, Liu B, Zhao X L and Yi L. 2011. Spatial and temporal change of wetlands in Bohai rim during 2000-2008: An analysis based on satellite images. Journal of Remote Sensing, 15(1): 183–200 (
  39. 39.
    Wu J H, Zhang S, Jiang Y, Kang M Y and Qiu Y. 2004. Plant Geography. 4th ed. Beijing: Higher Education Press
  40. 40.
    Xiao J C, Ou W X and Fu H Y. 2013. Land cover classification of Yancheng Coastal Natural Wetlands based on BP neural network and ETM+ remote sensing data. Acta Ecologica Sinica, 33(23): 7496–7504 (
  41. 41.
    Zhang C, Zang S Y, Jin Z and Zhang Y H. 2011. Remote sensing classification for Zhalong Wetlands based on support vector machine. Wetland Science, 9(3): 263–269 (
  42. 42.
    Zhang M, Zeng Y N and Zhu Y S. 2017. Wetland mapping of Donting Lake Basin based on time-series MODIS data and object-oriented method. Journal of Remote Sensing, 21(3): 479–492 (
  43. 43.
    Zhang S W, Yan F Q, Yu L X, Bu K, Yang J C and Chang L P. 2013. Application of remote sensing technology to wetland research. Scientia Geographica Sinica, 33(11): 1406–1412 (
  44. 44.
    Zhang X L, Ye S Y, Yin P and Chen D J. 2009. Characters and successions of natural wetland vegetation in Yellow River Delta. Ecology and Environmental Sciences, 18(1): 292–298 (
  45. 45.
    Zheng S D, Zheng J H, Shi M H, Guo B L, Sen B T, Sun Z Q, Jia X G and Li X J. 2014. Classification of cultivated Chinese medicinal plants based on fractal theory and gray level co-occurrence matrix textures. Journal of Remote Sensing, 18(4): 868–886 (
  46. 46.
    Zheng Y, Wu B F and Zhang M. 2017. Estimating the above ground biomass of winter wheat using the Sentinel-2 data. Journal of Remote Sensing, 21(2): 318–328 (

Leer el texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website